Halluminate is building reinforcement learning environments and benchmarks to help AI models do knowledge work beyond coding, starting with finance. With a team of fewer than 10 people, the company works with four of the top five closed-source US AI labs and recently raised a $30M Series A. In this fireside, co-founders @Jerr_Wu and @wgm752 sit down with YC's @xuster to share how testing browser agents led them to building the environments models now use to practice tasks like financial modeling. They explain why better verification, not just more training tasks, is critical to making models smarter, and how that work could eventually lead to simulated companies where teams of AI agents learn to work together. 00:00 — Halluminate’s Pivot From Evals to Training Environments 03:48 — Teaching AI to Do Financial Work 07:13 — Why Verification Matters More Than Volume 10:13 — From Finance to Simulated Companies 13:30 — AI Safety and Building the Team

We've raised a $30M to build the data research lab for knowledge work. We're hiring across engineering, research, ops, and more. Join us: jobs.ashbyhq.com/halluminate

We made a bet at @Halluminate in mid-2025 that Finance would be the next big field after coding that would really take off from agent usage. Few reasons for this: 1. Finance is the world's largest knowledge work industry 2. The outputs are semi-verifiable (which has some nice properties for RL) 3. Financial reasoning generalizes to a lot of other knowledge work domains the same way SWE agent capabilities generalized to a lot of other engineering domains (ex. CAD, MLE). Almost all my peers in banking have become increasingly AI-pilled in how they do work (similar to SWEs in Dec 2025 with Claude Code) and there's so much more progress to come soon. Its even more exciting to see our hard work in RL Envs/Benchmarking play out in the hands of consumers. If you'd like to contribute to building Financial Superintelligence we're hiring!

Although there may be some value in training on alignment specific RL Envs, my intuition is that the vast majority of "alignment" gains will come from hardening and increasing QA/QC across all RL Envs horizontally. Catching things like: - Impossible Tasks (ex. CyberGym Attack) - Escapable Sandboxes - Unintended code execution - Broken verifiers / non-comprehensive verification Will do more in aggregate than specifically trying to train on alignment RL Envs. Its like plugging one hole while many others exist. To put simply: this is a horizontal problem across the whole data/RL Env industry that every player needs to take super seriously. We all have a responsibility to play.

Halluminate Department of Research (HDR) here! We'll see you at EMNLP 2026 in Budapest with an oral presentation of our DealTrace benchmark, by Alinа Hyk, at FinNLP-2026 workshop🎉 Deal analysis is one of the most demanding tasks an AI agent can take on. The agent has to read a long selling document against the Excel model behind it, rebuild the forecast, and defend an investment recommendation, and a mistake at any step can carry through to the final answer without being visible in it. DealTrace was built to make that visible; we evaluated six frontier models under different levels of scaffolding, from no guidance to a structured method with a critique pass, on real private equity and M&A deal packages. Every claim in the recommendation is traced to the step that produced it, so the benchmark shows where an analysis fails rather than only scoring the final answer. If you work on agentic evaluation or AI in finance, we are looking forward to connecting at the conference; reach out!

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Price of raw tokens are much lower than price of built RL tasks on top of tokens (maybe 5-10%) Raw tokens are valuable but increasingly commoditized in the face of growing broker market. The value is in the refinement stages that turn raw tokens into usable environments. This is a complex supply chain of technology, engineering, research, ops, and taste.